Robust SLAM is a line of research that explicitly use back-end approaches to deal with outliers in the data [1]}, [2]}, [3]}.
In robust SLAM, most of the object measurements are already correctly associated to unique identifiers.
when some measurement is incorrectly associated, it will be inconsistent with other object measurements of the same identifier. Robust SLAM instead maximizes a set of measurements that are consistent with each other in both identifiers and predicted locations.
Only the consistent measurements are plugged into a SLAM solver to recover the robot poses and landmark locations.
